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Abstract

In the near future, multimedia will form the majority of Internet traffic and
the most popular standard used to transport and view video is MPEG. The MPEG
media content data is in the form of a time-series representing frame/VOP sizes.
This time-series is extremely noisy and analysis shows that it has very long-range
time dependency making it even harder to predict than any typical time-series. This
work is an effort to develop multi-step-ahead predictors for the moving averages of
frame/VOP sizes in MPEG-coded video streams.
In this work, both linear and non-linear system identification tools are used to
solve the prediction problem, and their performance is compared. Linear modeling is
done using Auto-Regressive Exogenous (ARX) models and for non linear modeling,
Artificial Neural Networks (ANN) are employed. The different ANN architectures
used in this work are Feed-forward Multi-Layer Perceptron (FMLP) and Recurrent
Multi-Layer Perceptron (RMLP).
Recent researches by Adas (October 1998), Yoo (March 2002) and Bhattacharya
et al. (August 2003) have shown that the multi-step-ahead prediction of individual
frames is very inaccurate. Therefore, for this work, we predict the moving average
of the frame/VOP sizes instead of individual frame/VOPs. Several multi-step-ahead
predictors are developed using the aforementioned linear and non-linear tools for
two/four/six/ten-step-ahead predictions of the moving average of the frame/VOP
size time-series of MPEG coded video source traffic.
The capability to predict future frame/VOP sizes and hence the bit rates will
enable more effective bandwidth allocation mechanism, assisting in the development
of advanced source control schemes needed to control multimedia traffic over wide
area networks, such as the Internet.